Biomedical Image Segmentation by Deep Learning Methods

Abstract Background With the development of deep learning (DL), more and more methods based on deep learning are proposed and achieve state-of-the-art performance in biomedical image segmentation. However, these methods are usually complex and require the support of powerful computing resources. According to the actual situation, it is impractical that we use huge computing resources in clinical situations. Thus, it is significant to develop accurate DL based biomedical image segmentation methods which depend on resources-constraint computing. Results A lightweight and multiscale network called PyConvU-Net is proposed to potentially work with low-resources computing. Through strictly controlled experiments, PyConvU-Net predictions have a good performance on three biomedical image segmentation tasks with the fewest parameters. Conclusions Our experimental results preliminarily demonstrate the potential of proposed PyConvU-Net in biomedical image segmentation with resources-constraint computing.

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Region-of-Interest-Based Cardiac Image Segmentation with Deep Learning

Applied Sciences ◽

10.3390/app11041965 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1965

Author(s):

Raul-Ronald Galea ◽

Laura Diosan ◽

Anca Andreica ◽

Loredana Popa ◽

Simona Manole ◽

...

Keyword(s):

Image Segmentation ◽

Deep Learning ◽

Diagnostic System ◽

Region Of Interest ◽

General Purpose ◽

Medical Image Segmentation ◽

Dice Similarity Coefficient ◽

Learning Methods ◽

Study Results ◽

Whole Heart

Despite the promising results obtained by deep learning methods in the field of medical image segmentation, lack of sufficient data always hinders performance to a certain degree. In this work, we explore the feasibility of applying deep learning methods on a pilot dataset. We present a simple and practical approach to perform segmentation in a 2D, slice-by-slice manner, based on region of interest (ROI) localization, applying an optimized training regime to improve segmentation performance from regions of interest. We start from two popular segmentation networks, the preferred model for medical segmentation, U-Net, and a general-purpose model, DeepLabV3+. Furthermore, we show that ensembling of these two fundamentally different architectures brings constant benefits by testing our approach on two different datasets, the publicly available ACDC challenge, and the imATFIB dataset from our in-house conducted clinical study. Results on the imATFIB dataset show that the proposed approach performs well with the provided training volumes, achieving an average Dice Similarity Coefficient of the whole heart of 89.89% on the validation set. Moreover, our algorithm achieved a mean Dice value of 91.87% on the ACDC validation, being comparable to the second best-performing approach on the challenge. Our approach provides an opportunity to serve as a building block of a computer-aided diagnostic system in a clinical setting.

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Biomedical Image Segmentation via Representative Annotation

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33015901 ◽

2019 ◽

Vol 33 ◽

pp. 5901-5908 ◽

Cited By ~ 8

Author(s):

Hao Zheng ◽

Lin Yang ◽

Jianxu Chen ◽

Jun Han ◽

Yizhe Zhang ◽

...

Keyword(s):

Image Segmentation ◽

Deep Learning ◽

Active Learning ◽

Iterative Process ◽

Image Data ◽

Manual Annotation ◽

Convolutional Network ◽

Biomedical Image ◽

Image Patches ◽

Representative Image

Deep learning has been applied successfully to many biomedical image segmentation tasks. However, due to the diversity and complexity of biomedical image data, manual annotation for training common deep learning models is very timeconsuming and labor-intensive, especially because normally only biomedical experts can annotate image data well. Human experts are often involved in a long and iterative process of annotation, as in active learning type annotation schemes. In this paper, we propose representative annotation (RA), a new deep learning framework for reducing annotation effort in biomedical image segmentation. RA uses unsupervised networks for feature extraction and selects representative image patches for annotation in the latent space of learned feature descriptors, which implicitly characterizes the underlying data while minimizing redundancy. A fully convolutional network (FCN) is then trained using the annotated selected image patches for image segmentation. Our RA scheme offers three compelling advantages: (1) It leverages the ability of deep neural networks to learn better representations of image data; (2) it performs one-shot selection for manual annotation and frees annotators from the iterative process of common active learning based annotation schemes; (3) it can be deployed to 3D images with simple extensions. We evaluate our RA approach using three datasets (two 2D and one 3D) and show our framework yields competitive segmentation results comparing with state-of-the-art methods.

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Performance Analysis of Deep Learning Models for Biomedical Image Segmentation

Deep Learning for Biomedical Applications ◽

10.1201/9780367855611-5 ◽

2021 ◽

pp. 83-100

Author(s):

T. K. Saj Sachin ◽

V. Sowmya ◽

K. P. Soman

Keyword(s):

Image Segmentation ◽

Deep Learning ◽

Performance Analysis ◽

Learning Models ◽

Biomedical Image

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DDeep3M: Docker-powered deep learning for biomedical image segmentation

Journal of Neuroscience Methods ◽

10.1016/j.jneumeth.2020.108804 ◽

2020 ◽

Vol 342 ◽

pp. 108804

Author(s):

Xinglong Wu ◽

Shangbin Chen ◽

Jin Huang ◽

Anan Li ◽

Rong Xiao ◽

...

Keyword(s):

Image Segmentation ◽

Deep Learning ◽

Biomedical Image

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Brief review on learning-based methods for optical tomography

Journal of Innovative Optical Health Sciences ◽

10.1142/s1793545819300118 ◽

2019 ◽

Vol 12 (06) ◽

pp. 1930011

Author(s):

Lin Zhang ◽

Guanglei Zhang

Keyword(s):

Deep Learning ◽

Optical Tomography ◽

Fluorescence Molecular Tomography ◽

Learning Methods ◽

Bioluminescence Tomography ◽

Biomedical Image ◽

Histopathological Image ◽

Acquisition Strategy ◽

Magnetic Resonance Imaging Mri ◽

Data Acquisition Strategy

Learning-based methods have been proved to perform well in a variety of areas in the biomedical field, such as biomedical image segmentation, and histopathological image analysis. Deep learning, as the most recently presented approach of learning-based methods, has attracted more and more attention. For instance, massive researches of deep learning methods for image reconstructions of computed tomography (CT) and magnetic resonance imaging (MRI) have been reported, indicating the great potential of deep learning for inverse problems. Optical technology-related medical imaging modalities including diffuse optical tomography (DOT), fluorescence molecular tomography (FMT), bioluminescence tomography (BLT), and photoacoustic tomography (PAT) are also dramatically innovated by introducing learning-based methods, in particular deep learning methods, to obtain better reconstruction results. This review depicts the latest researches on learning-based optical tomography of DOT, FMT, BLT, and PAT. According to the most recent studies, learning-based methods applied in the field of optical tomography are categorized as kernel-based methods and deep learning methods. In this review, the former are regarded as a sort of conventional learning-based methods and the latter are subdivided into model-based methods, post-processing methods, and end-to-end methods. Algorithm as well as data acquisition strategy are discussed in this review. The evaluations of these methods are summarized to illustrate the performance of deep learning-based reconstruction.

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